Document Representations to Improve Topic Modelling

Abstract

Each and every day, huge amount of information are collected from web applications. So it is difficult to understand or detect what the whole information is all about. To detect, understand and summarise the whole information, it requires some specific tools and techniques like topic modelling, which helps to analyze and identify the crisp of the data. This paper implements the sparsity based document representation to improve Topic Modeling, it organizes the data with meaningful structure by using machine learning algorithms like LDA (Latent Dirichlet Allocation) and OMP (Orthogonal Matching Pursuit) algorithms. It identifies a documents belongs to which topic as well as similarity between documents in an existing dictionary. The OMP(Orthogonal Matching Pursuit) algorithm is the best algorithm for sparse approximation With better accuracy. OMP(Orthogonal Matching Pursuit) algorithm can identify the topics to which the input document[Y] is mostly related to across a large collection of text documents present in a dictionary.

Keywords: topic modeling; sparse matrix; dictionary representation; Latent Dirichlet Allocation; Orthogonal Matching Pursuit; clustering; TF-IDF; document detection